We proposed an augmented cepstral mean normalization
algorithm that differentiates noise and speech during
normalization, and computes a different mean for each. The new
procedure reduced the error rate slightly for the case of sameenvironment
testing, and significantly reduced the error rate by
25{\%} when an environmental mismatch exists over the case of
standard cepstral mean normalization.